User-centric Explanation Strategies for Interactive Recommenders

With the pervasive usage of recommendation systems across various domains, there is a growing need for transparent and convincing interactions to build a rapport with the system users. Incorporating explainability into recommendation systems has become a promising strategy to bolster user trust and sociability. This study centers on recommendation systems that leverage varying explainability techniques to cultivate trust by delivering comprehensible customized explanations for the given recommendations. Accordingly, we propose two explanation methods aligning with a cluster-based recommendation strategy.